A Decision Tree Approach to Estimate Permanent Residents Using Remote Sensing Data in Lebanese Municipalities
Population estimation using Geographic Information System (GIS) and remote sensing faces many obstacles such as the determination of permanent residents. A permanent resident is an individual who stays and works during all four seasons in his village. So, all those who move towards other cities or villages are excluded from this category. The aim of this study is to identify the factors affecting the percentage of permanent residents in a village and to determine the attributed weight to each factor. To do so, six factors have been chosen (slope, precipitation, temperature, number of services, time to Central Business District (CBD) and the proximity to conflict zones) and each one of those factors has been evaluated using one of the following data: the contour lines map of 50 m, the precipitation map, four temperature maps and data collected through surveys. The weighting procedure has been done using decision tree method. As a result of this procedure, temperature (50.8%) and percentage of precipitation (46.5%) are the most influencing factors.
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 Hsu, S. Y., 1971, “Population Estimation,” Photogrammetric Engineering, 37:449-454.
 Hsu, S. Y., 1973, “Population Estimation from ERTS Imagery: Methodology and Evaluation,” Proceedings of the American Society of Photogrammetry 39th Annual Meeting, 583-591.
 Kraus, S. P., Senger, L. W. and J. M. Ryerson, 1974, “Estimating Population from Photographically Determined Residential Land Use Types,” Remote Sensing of Environment, 3(1):35-42.
 Langford, M. and D. J. Unwin, 1994, “Generating and Mapping Population-Density Surfaces within a Geographical Information-System,” Cartographic Journal, 31(1):21-26.
 Harvey, J. T., 2002, “Population Estimation Models Based on Individual TM Pixels,” Photogrammetric Engineering and Remote Sensing, 68(11):1181-1192
 Rindfuss, R., Stern, P., 1998, Linking Remote Sensing and Social Science: The need and the challenges, The National Academy Press, Available online at: http://www.csiss.org/SPACE/workshops/2006/OU/reading/RindfussandStern.pdf (Accessible: 5/5/2019).
 Dahan, Haim, Cohen, Shahar, Rokach, Lior, Maimon, Oded, 2014, Proactive Data Mining with Decision Trees (SpringerBriefs in Electrical and Computer Engineering) 2014th Edition, Springer.
 Lees, B., Ritman, K., 1991, Decision tree and Rule-Induction approach to integration of Remotely sensed and GIS data in mapping vegetation in Disturbed or Hilly Environments.
 Pal, M., Mather, P., 2003, An assessment of the effectiveness of decision tree methods for land cover classification, Remote Sensing of Environment, 86, pp. 554-565.
 Bogaert, J., Ceulemans, R., Eysenrode, D., 2004, Decision Tree Algorithm for Detection of Spatial Processes in Landscape Transformation, Environmental Management, Vol. 33, No. 1, pp. 62–73.
 Ballestores Jr, F., Qiu, Zeyuan, 2012, Proceedings of the International Academy of Ecology and Environmental Sciences, 2012, 2(2), 53-69.